Video retail checkout activity analysis systems operate in terms of one or more regions of interest (ROI) which correspond to salient regions in the checkout area (for example, entry belt, barcode scanner, exit belt, cashier work area, register, etc.).
However, manual calibration of these ROIs is very time-consuming and quickly becomes difficult to manage as the number of stores and/or lanes increases. Additionally, this calibration needs to be performed any time the corresponding camera moves (either intentionally or unintentionally), is blocked or enters an operational state that renders video analytics non-functional (for example, loss of focus, damaged lens, etc.). When conditions such as these occur, the ROIs are no longer positioned in the correct area or are no longer meaningful. As a result, the system will perform sub-optimally or give incorrect results. Furthermore, a human may remain unaware of an issue for an extended period of time.
Existing approaches include an initial calibration that is a manual process performed by a human, where each human specifies each ROI on each checkout station according to the initial camera field of view. As noted above, this is very time-consuming proportional to the number of checkout stations that need to be configured. Additionally, existing approaches can also include calibration subsequent to installment via periodic manual inspection. This, however, also includes the same disadvantages noted above multiplied by the frequency of inspection. Additionally, such disadvantages become magnified when, for example, the number of stores gets into the hundreds and thousands, and where each store may have as many as 50 checkout lanes.